We introduce algorithms for learning nonlinear dynamical systems of theform $ x_ {t+
1}=\sigma (\Theta {} x_t)+\varepsilon_t $, where $\Theta $ is a weightmatrix, $\sigma $ is a …

While the identification of nonlinear dynamical systems is a fundamental building block of
model-based reinforcement learning and feedback control, its sample complexity is only …

We consider the problem of learning a nonlinear dynamical system governed by a nonlinear
state equation ht+ 1= ϕ (ht, ut; θ)+ wt. Here θ is the unknown system dynamics, ht is the …

While the identification of nonlinear dynamical systems is a fundamental building block of
model-based reinforcement learning and feedback control, its sample complexity is only …

We consider the setting of vector valued non-linear dynamical systems $ X_ {t+ 1}=\phi
(A^{*} X_t)+\eta_t $, where $\eta_t $ is unbiased noise and $\phi:\mathbb {R}\to\mathbb {R} …

Bilinear dynamical systems are ubiquitous in many different domains and they can also be
used to approximate more general control-affine systems. This motivates the problem of …

We consider the problem of learning a realization of a partially observed dynamical system
with linear state transitions and bilinear observations. Under very mild assumptions on the …

Identification of nonlinear dynamical systems is crucial across various fields, facilitating tasks
such as control, prediction, optimization, and fault detection. Many applications require …

We study the problem of learning to predict the next state of a dynamical system when the
underlying evolution function is unknown. Unlike previous work, we place no parametric …

We consider the problem of learning a realization of a partially observed bilinear dynamical
system (BLDS) from noisy input-output data. Given a single trajectory of input-output …